ICT – Integrating Computers in Teaching: Creating a Computer-Based Language-Learning Environment
Bibliographic record
Abstract
This book examines the role of computers in language learning and teaching in higher education. In particular, it considers the pedagogical and practical value of designing a language-learning environment around computer technology. Whereas considerable research has already been undertaken in analysing the value of individual computer tools and packages (such as e-mail), the study gives a broad appraisal of their individual and collective value, without being too exhaustive. Using quantitative and qualitative data, based on research visits to three universities, Ulster, Cambridge and Toronto, this study provides examples of effective practice in the area of the exploitation of Information and Communication Technology for language learning and teaching. It draws on the experience of these three institutions, as well as the findings of current literature in this area, in order to establish a set of essential criteria that institutions need to meet when creating a computer-based environment. Although these criteria are based on experience with language-learning environments, they are essentially generic in nature and may be applied to other computer-based learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".